Breast Cancer Study (breast + cancer_study)

Distribution by Scientific Domains
Mathematics and Statistics100%

Selected Abstracts

Age,environment model for breast cancer

ENVIRONMETRICS, Issue 3 2004
Nobutane Hanayama
Abstract In the field of breast cancer study, it has become accepted that crucial exposures to environmental risks might have occurred years before a malignant tumor is evident in human breasts, while age factors such as ages at menstruation have been known as risks for the disease already. To project trends in two such kinds of risks for the disease, the concept of environment effects is introduced for (age, period)-specific breast cancer mortality rates. Also, a new model, named the age,environment (AE) model, which assumes that the logarithm of the expected rate is a linear function of environment effects and age effects, is proposed. It is shown that, although environment effects have different meanings from period effects or cohort effects, in the age,period,cohort (APC) model, the range space of the design matrix for the AE model is included in that for APC model. It is seen, however, that the AE model provides a better fit to the data for females in Japan and the four Nordic countries than does the APC model in terms of AIC. From the results of ML estimation of the parameters in the AE model based on the data obtained in Japan, we see high levels of environment effects associated with the Sino,Japanese war, World War II and the environmental pollution due to the economy in the recovery period from the defeat. Besides, from those based on the data obtained in the four Nordic countries, we see high levels of environment effects associated with the environment becoming worse after the year of Helsinki Olympics and low levels of them associated with the period including the year of ,Miracle of the Winter War' in Finland. Copyright © 2004 John Wiley & Sons, Ltd. [source]

A Frailty-Model-Based Approach to Estimating the Age-Dependent Penetrance Function of Candidate Genes Using Population-Based Case-Control Study Designs: An Application to Data on the BRCA1 Gene

BIOMETRICS, Issue 4 2009
Lu Chen
Summary The population-based case,control study design is perhaps one of, if not the most, commonly used designs for investigating the genetic and environmental contributions to disease risk in epidemiological studies. Ages at onset and disease status of family members are routinely and systematically collected from the participants in this design. Considering age at onset in relatives as an outcome, this article is focused on using the family history information to obtain the hazard function, i.e., age-dependent penetrance function, of candidate genes from case,control studies. A frailty-model-based approach is proposed to accommodate the shared risk among family members that is not accounted for by observed risk factors. This approach is further extended to accommodate missing genotypes in family members and a two-phase case,control sampling design. Simulation results show that the proposed method performs well in realistic settings. Finally, a population-based two-phase case,control breast cancer study of the BRCA1 gene is used to illustrate the method. [source]

Exact Log-Rank Tests for Unequal Follow-Up

BIOMETRICS, Issue 4 2003
Georg Heinze
Summary. The asymptotic log-rank and generalized Wilcoxon tests are the standard procedures for comparing samples of possibly censored survival times. For comparison of samples of very different sizes, an exact test is available that is based on a complete permutation of log-rank or Wilcoxon scores. While the asymptotic tests do not keep their nominal sizes if sample sizes differ substantially, the exact complete permutation test requires equal follow-up of the samples. Therefore, we have developed and present two new exact tests also suitable for unequal follow-up. The first of these is an exact analogue of the asymptotic log-rank test and conditions on observed risk sets, whereas the second approach permutes survival times while conditioning on the realized follow-up in each group. In an empirical study, we compare the new procedures with the asymptotic log-rank test, the exact complete permutation test, and an earlier proposed approach that equalizes the follow-up distributions using artificial censoring. Results confirm highly satisfactory performance of the exact procedure conditioning on realized follow-up, particularly in case of unequal follow-up. The advantage of this test over other options of analysis is finally exemplified in the analysis of a breast cancer study. [source]

A Solution to the Problem of Monotone Likelihood in Cox Regression

BIOMETRICS, Issue 1 2001
Georg Heinze
Summary. The phenomenon of monotone likelihood is observed in the fitting process of a Cox model if the likelihood converges to a finite value while at least one parameter estimate diverges to ±,. Monotone likelihood primarily occurs in small samples with substantial censoring of survival times and several highly predictive covariates. Previous options to deal with monotone likelihood have been unsatisfactory. The solution we suggest is an adaptation of a procedure by Firth (1993, Biometrika80, 27,38) originally developed to reduce the bias of maximum likelihood estimates. This procedure produces finite parameter estimates by means of penalized maximum likelihood estimation. Corresponding Wald-type tests and confidence intervals are available, but it is shown that penalized likelihood ratio tests and profile penalized likelihood confidence intervals are often preferable. An empirical study of the suggested procedures confirms satisfactory performance of both estimation and inference. The advantage of the procedure over previous options of analysis is finally exemplified in the analysis of a breast cancer study. [source]